代码更新：
1. 仿真部分代码修改为GPU 2. 删除了原CPU的仿真代码 3. GPU代码部分，由 SimulationSAR 文件，移动到 BaseLibraryCPP 文件中 4. 补充了成像平面的 matlab 代码Release
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Subproject commit 458be41d96524bbdad14aaa0b6184a5de387e7e5
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Subproject commit ef94aa388ae2ed85e970ff4f857450a5f28cf0b2
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239
README.md
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README.md
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# RasterProcessTool
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# RasterProcessTool
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extern "C" void AddCUDA(void* aPtr, void* bptr, void* cptr, long member, LAMPGPUDATETYPE datetype) {
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int blockSize = 256; // 每个块的线程数
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int numBlocks = (member + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
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}
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// CUDA 核函数
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__global__ void computeDistanceAndEchoID(float* antPx, float* antPy, float* antPz,
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float* img_x, float* img_y, float* img_z,
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complexfloat* echopluse, complexfloat* imgarr,
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long rowcount, long colcount, long prfid,
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float Rnear, float fs, float factorj) {
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long idx = blockIdx.x * blockDim.x + threadIdx.x;
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// 确保线程索引有效
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if (idx < rowcount * colcount) {
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// 计算距离
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float dx = antPx[prfid] - img_x[idx];
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float dy = antPy[prfid] - img_y[idx];
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float dz = antPz[prfid] - img_z[idx];
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float imgR = sqrt(dx * dx + dy * dy + dz * dz);
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// 计算 EchoID
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long echoID = floor(((imgR - Rnear) * 2 / LIGHTSPEED) * fs);//回波坐标
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float Rftj = imgR * factorj; // 校正
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//printf("%d:(%f,%f),%f,%f |||||, %f, %f, %f, %f, %f, %f, %f\n", idx, echopluse[echoID].real, Rftj, imgR, fs,
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// antPx[prfid], antPy[prfid], antPz[prfid], img_x[idx], img_y[idx], img_z[idx], imgR);
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if (echoID < 0 || echoID >= colcount) {
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}
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else {
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complexfloat Rphi{ 0,Rftj };
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Rphi = expComplex(Rphi);
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imgarr[idx] = addComplex(imgarr[idx], mulComplex(echopluse[echoID], Rphi));
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}
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}
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}
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void TBPImageGPUBlock(float* antPx, float* antPy, float* antPz, float* img_x, float* img_y, float* img_z,
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std::shared_ptr<std::complex<double>> echoArr, long prfcount, long plusecount,
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std::shared_ptr<std::complex<double>> imageArr,
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float freq, float fs, float Rnear, float Rfar, float factorj,
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long startline, long stepline,
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long rowcount, long colcount) {
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long pixelcount = rowcount * colcount;
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complexfloat* h_echopluse;
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complexfloat* h_imgarr;
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cudaMallocHost(&h_echopluse, sizeof(float) * 2 * plusecount); // 单个传感器的位置
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cudaMallocHost(&h_imgarr, sizeof(float) * 2 * stepline * colcount);
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for (long i = 0; i < stepline; i++) {
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long rid = startline + i;
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for (long j = 0; j < colcount; j++) {
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h_imgarr[i * colcount + j].real = imageArr.get()[rid * colcount + j].real();
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h_imgarr[i * colcount + j].imag = imageArr.get()[rid * colcount + j].imag();
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}
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}
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std::cout << "h_imgarr init finished!!" << std::endl;
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float* h_antPx, * h_antPy, * h_antPz;
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cudaMallocHost(&h_antPx, sizeof(float) * prfcount); // 单个传感器的位置
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cudaMallocHost(&h_antPy, sizeof(float) * prfcount);
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cudaMallocHost(&h_antPz, sizeof(float) * prfcount);
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// 初始化
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for (long i = 0; i < prfcount; i++) {
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h_antPx[i] = antPx[i];
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h_antPy[i] = antPy[i];
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h_antPz[i] = antPz[i];
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}
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float* h_img_x, * h_img_y, * h_img_z;
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cudaMallocHost(&h_img_x, sizeof(float) * stepline * colcount);
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cudaMallocHost(&h_img_y, sizeof(float) * stepline * colcount);
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cudaMallocHost(&h_img_z, sizeof(float) * stepline * colcount);
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// 初始化
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long rid = 0;
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for (long i = 0; i < stepline; i++) {
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rid = startline + i;
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for (long j = 0; j < colcount; j++) {
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h_img_x[i * colcount + j] = img_x[rid * colcount + j];
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h_img_y[i * colcount + j] = img_y[rid * colcount + j];
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h_img_z[i * colcount + j] = img_z[rid * colcount + j];
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}
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}
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std::cout << "h_img_x init finished!!" << std::endl;
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// 分配设备内存
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float* d_antPx, * d_antPy, * d_antPz, * d_img_x, * d_img_y, * d_img_z;
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complexfloat* d_echopluse;
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complexfloat* d_imgarr;
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cudaMalloc(&d_echopluse, sizeof(float) * 2 * plusecount);
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cudaMalloc(&d_imgarr, sizeof(float) * 2 * stepline * colcount);
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cudaMalloc(&d_antPx, sizeof(float) * prfcount);
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cudaMalloc(&d_antPy, sizeof(float) * prfcount);
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cudaMalloc(&d_antPz, sizeof(float) * prfcount);
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cudaMalloc(&d_img_x, sizeof(float) * rowcount * colcount);
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cudaMalloc(&d_img_y, sizeof(float) * rowcount * colcount);
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cudaMalloc(&d_img_z, sizeof(float) * rowcount * colcount);
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// 将数据从主机拷贝到设备
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cudaMemcpy(d_antPx, h_antPx, sizeof(float) * prfcount, cudaMemcpyHostToDevice);
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cudaMemcpy(d_antPx, h_antPx, sizeof(float) * prfcount, cudaMemcpyHostToDevice);
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cudaMemcpy(d_antPy, h_antPy, sizeof(float) * prfcount, cudaMemcpyHostToDevice);
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cudaMemcpy(d_antPz, h_antPz, sizeof(float) * prfcount, cudaMemcpyHostToDevice);
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cudaMemcpy(d_img_x, h_img_x, sizeof(float) * rowcount * colcount, cudaMemcpyHostToDevice);
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cudaMemcpy(d_img_y, h_img_y, sizeof(float) * rowcount * colcount, cudaMemcpyHostToDevice);
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cudaMemcpy(d_img_z, h_img_z, sizeof(float) * rowcount * colcount, cudaMemcpyHostToDevice);
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cudaMemcpy(d_imgarr, h_imgarr, sizeof(float) * 2 * stepline * colcount, cudaMemcpyHostToDevice);
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int blockSize = 256; // 每个块的线程数
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int numBlocks = (pixelcount + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
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long eid = 0;
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std::complex<double> Rphi;
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for (long prfid = 0; prfid < prfcount; prfid++) {
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for (long i = 0; i < plusecount; i++) {
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h_echopluse[i].real = echoArr.get()[prfid * plusecount + i].real();
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h_echopluse[i].imag = echoArr.get()[prfid * plusecount + i].imag();
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}
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cudaMemcpy(d_echopluse, h_echopluse, sizeof(float) * 2 * plusecount, cudaMemcpyHostToDevice);
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computeDistanceAndEchoID << < numBlocks, blockSize >> > (d_antPx, d_antPy, d_antPz,
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d_img_x, d_img_y, d_img_z,
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d_echopluse, d_imgarr,
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rowcount, colcount, prfid,
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Rnear, fs, factorj);
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cudaDeviceSynchronize();// 等待所有设备任务完成
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if (prfid % 100 == 0) {
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//std::cout << "\rprf " << prfid <<"/"<< prfcount << "\t\t\t";
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}
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//cudaMemcpy(h_echopluse, d_echopluse, sizeof(float) * 2 * stepline * colcount, cudaMemcpyDeviceToHost);
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}
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std::cout << std::endl;
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// GPU -> CPU
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cudaMemcpy(h_imgarr, d_imgarr, sizeof(float) * 2 * stepline * colcount, cudaMemcpyDeviceToHost);
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for (long i = 0; i < stepline; i++) {
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long rid = startline + i;
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for (long j = 0; j < colcount; j++) {
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imageArr.get()[rid * colcount + j] = std::complex<double>(h_imgarr[i * colcount + j].real, h_imgarr[i * colcount + j].imag);
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}
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}
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// 清理资源
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cudaFree(d_antPx);
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cudaFree(d_antPy);
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cudaFree(d_antPz);
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cudaFree(d_img_x);
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cudaFree(d_img_y);
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cudaFree(d_img_z);
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cudaFree(d_echopluse);
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cudaFree(d_imgarr);
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cudaFreeHost(h_antPx);
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cudaFreeHost(h_antPy);
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cudaFreeHost(h_antPz);
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cudaFreeHost(h_img_x);
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cudaFreeHost(h_img_y);
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cudaFreeHost(h_img_z);
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cudaFreeHost(h_echopluse);
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cudaFreeHost(h_imgarr);
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std::cout << "end GPU" << std::endl;
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}
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void RTPC(float* antx, float* anty, float* antz,
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float* demx, float* demy, float* demz,
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float* demslopex, float* demslopey, float* demslopez
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) {}
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@ -179,6 +179,7 @@
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<QtMoc Include="SimulationSAR\QToolAbstract.h" />
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<QtMoc Include="RegisterToolbox.h" />
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<QtMoc Include="SimulationSAR\QSimulationBPImage.h" />
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<ClInclude Include="BaseLibraryCPP\GPUTool.cuh" />
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<ClInclude Include="SimulationSAR\TBPImageAlgCls.h" />
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<QtMoc Include="QSimulationRTPCGUI.h" />
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<QtMoc Include="GF3ProcessToolbox\QOrthSlrRaster.h" />
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<QtMoc Include="QMergeRasterProcessDialog.h" />
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</ItemGroup>
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<ItemGroup>
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<CudaCompile Include="SimulationSAR\TBPGPU.cu">
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<FileType>Document</FileType>
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<CudaCompile Include="BaseLibraryCPP\GPUTool.cu">
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<ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
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</ExcludedFromBuild>
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</CudaCompile>
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</ItemGroup>
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<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
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<ClInclude Include="SimulationSAR\TBPImageAlgCls.h">
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<Filter>SimulationSAR</Filter>
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</ClInclude>
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<ClInclude Include="BaseLibraryCPP\GPUTool.cuh">
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<Filter>BaseLibraryCPP</Filter>
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</ClInclude>
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</ItemGroup>
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<ItemGroup>
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<QtMoc Include="QMergeRasterProcessDialog.h">
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</QtUic>
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</ItemGroup>
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<ItemGroup>
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<CudaCompile Include="SimulationSAR\TBPGPU.cu">
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<Filter>SimulationSAR</Filter>
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<CudaCompile Include="BaseLibraryCPP\GPUTool.cu">
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<Filter>BaseLibraryCPP</Filter>
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</CudaCompile>
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</ItemGroup>
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</Project>
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TBPimag.setEchoL0(echoL0ds);
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TBPimag.setImageL1(imagL1);
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long cpucore_num = std::thread::hardware_concurrency();
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TBPimag.setGPU(true);
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TBPimag.Process(cpucore_num);
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QMessageBox::information(this,u8"成像",u8"成像结束");
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}
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void QSimulationBPImage::onbtnrejected()
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<height>30</height>
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</size>
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</property>
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<property name="text">
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<string>D:\Programme\vs2022\RasterMergeTest\TestData\outData\GF3_Simulation.xml</string>
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</property>
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</widget>
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</item>
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<item>
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<height>30</height>
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</size>
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</property>
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<property name="text">
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<string>D:\Programme\vs2022\RasterMergeTest\TestData\outBPImage2\GF3_Simulation</string>
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</property>
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</widget>
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</item>
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<item>
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File diff suppressed because it is too large
Load Diff
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@ -613,6 +613,13 @@ ErrorCode AbstractRadiationPattern::getGainLinear(double& theta, double& phi, do
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}
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double AbstractRadiationPattern::getGainLearThetaPhi(double theta, double phi)
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{
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double gainvlaue = 0;
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this->getGainLinear(theta, phi, gainvlaue);
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return gainvlaue;
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}
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ErrorCode AbstractRadiationPattern::setGain(double& theta, double& phi, double& GainValue)
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{
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this->GainMap.push_back(RadiationPatternGainPoint{ theta,phi,GainValue });
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virtual std::vector<RadiationPatternGainPoint> getGainList();
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virtual ErrorCode getGain(double& theta, double& phi, double& GainValue);
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virtual ErrorCode getGainLinear(double& theta, double& phi, double& GainValue);
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double getGainLearThetaPhi(double theta, double phi);
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virtual ErrorCode setGain(double& theta, double& phi, double& GainValue);
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virtual ErrorCode RecontructGainMatrix(double threshold=-3);
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virtual std::vector<double> getThetas();
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///
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ErrorCode PolyfitSatelliteOribtModel::getAntnnaDirection(SatelliteOribtNode& node)
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{
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bool flag = false;
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double nexttime = node.time + 1e-6;
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SatelliteOribtNode node1 = this->getSatelliteOribtNode(nexttime, flag);
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// 1. 计算天线指向
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Eigen::Vector3d axisZ0 = { -1*node.Px ,-1 * node.Py,-1 * node.Pz}; // z 轴 --波位角为0时，天线指向的反方向
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Eigen::Vector3d axisX0 = { node.Vx ,node.Vy,node.Vz }; // x 轴 --飞行方向
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Eigen::Vector3d axisZ0 = { -1 * node.Px ,-1 * node.Py,-1 * node.Pz }; // z 轴 --波位角为0时，天线指向的反方向
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Eigen::Vector3d axisX0 = { (node1.Px - node.Px) , (node1.Py - node.Py), (node1.Pz - node.Pz) }; // x 轴 --飞行方向
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Eigen::Vector3d axisY0 = axisZ0.cross(axisX0); // y 轴 --右手定则 -- 初始坐标系
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double rotateAngle = this->RightLook ? -this->beamAngle : this->beamAngle; // 旋转角度 左（逆时针）：theta , 右（顺时针）： -theta
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// 1.2. 根据波位角，确定卫星绕X轴-飞行轴
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@ -240,7 +246,7 @@ ErrorCode PolyfitSatelliteOribtModel::getAntnnaDirection(SatelliteOribtNode& nod
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axisX0=rotateMatrixBeam*axisX0;
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// 1.3. 根据方位角，确定卫星绕Y轴旋转
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double azangle = node.AzAngle;
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Eigen::Matrix3d rotateMatrixAzAngle = rotationMatrix(axisY0, rotateAngle * d2r); // 旋转矩阵
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Eigen::Matrix3d rotateMatrixAzAngle = rotationMatrix(axisY0, azangle * d2r); // 旋转矩阵
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axisZ0 = rotateMatrixAzAngle * axisZ0; // 旋转矩阵
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axisY0 = rotateMatrixAzAngle * axisY0;
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axisX0 = rotateMatrixAzAngle * axisX0;
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@ -270,9 +276,13 @@ ErrorCode PolyfitSatelliteOribtModel::getAntnnaDirection(SatelliteOribtNode& nod
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ErrorCode PolyfitSatelliteOribtModel::getZeroDopplerAntDirect(SatelliteOribtNode& node)
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{
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bool flag = false;
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double nexttime = node.time + 1e-6;
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SatelliteOribtNode node1 = this->getSatelliteOribtNode(nexttime, flag);
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// 1. 计算天线指向
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Eigen::Vector3d axisZ0 = { -1 * node.Px ,-1 * node.Py,-1 * node.Pz }; // z 轴 --波位角为0时，天线指向的反方向
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Eigen::Vector3d axisX0 = { node.Vx ,node.Vy,node.Vz }; // x 轴 --飞行方向
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Eigen::Vector3d axisX0 = { (node1.Px-node.Px) , (node1.Py - node.Py) , (node1.Pz - node.Pz) }; // x 轴 --飞行方向
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Eigen::Vector3d axisY0 = axisZ0.cross(axisX0); // y 轴 --右手定则 -- 初始坐标系
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double rotateAngle = this->RightLook ? -this->beamAngle : this->beamAngle; // 旋转角度 左（逆时针）：theta , 右（顺时针）： -theta
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|
|
@ -284,7 +294,7 @@ ErrorCode PolyfitSatelliteOribtModel::getZeroDopplerAntDirect(SatelliteOribtNode
|
|||
axisX0 = rotateMatrixBeam * axisX0;
|
||||
// 1.3. 根据方位角，确定卫星绕Y轴旋转
|
||||
double azangle = 0;
|
||||
Eigen::Matrix3d rotateMatrixAzAngle = rotationMatrix(axisY0, rotateAngle * d2r); // 旋转矩阵
|
||||
Eigen::Matrix3d rotateMatrixAzAngle = rotationMatrix(axisY0, azangle * d2r); // 旋转矩阵
|
||||
axisZ0 = rotateMatrixAzAngle * axisZ0; // 旋转矩阵
|
||||
axisY0 = rotateMatrixAzAngle * axisY0;
|
||||
axisX0 = rotateMatrixAzAngle * axisX0;
|
||||
|
|
|
|||
|
|
@ -1,190 +0,0 @@
|
|||
#include <iostream>
|
||||
#include <memory>
|
||||
#include <cmath>
|
||||
#include <complex>
|
||||
#include <cuda_runtime.h>
|
||||
#include <cublas_v2.h>
|
||||
#include <device_launch_parameters.h>
|
||||
#include "BaseConstVariable.h"
|
||||
|
||||
|
||||
void checkCudaError(cudaError_t err, const char* msg) {
|
||||
if (err != cudaSuccess) {
|
||||
std::cerr << "CUDA error: " << msg << " (" << cudaGetErrorString(err) << ")" << std::endl;
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// CUDA 核函数
|
||||
__global__ void computeDistanceAndEchoID(float* antPx, float* antPy, float* antPz,
|
||||
float* img_x, float* img_y, float* img_z,
|
||||
float* imgR, float* imgEchoID,
|
||||
long rowcount, long colcount, long prfid,
|
||||
float Rnear, float fs) {
|
||||
int i = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
int j = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
|
||||
// 确保线程索引有效
|
||||
if (i < rowcount && j < colcount) {
|
||||
int idx = i * colcount + j;
|
||||
|
||||
// 计算距离
|
||||
float dx = antPx[prfid] - img_x[idx];
|
||||
float dy = antPy[prfid] - img_y[idx];
|
||||
float dz = antPz[prfid] - img_z[idx];
|
||||
imgR[idx] = sqrt(dx * dx + dy * dy + dz * dz);
|
||||
|
||||
// 计算 EchoID
|
||||
imgEchoID[idx] = (imgR[idx] - Rnear) / (2 * LIGHTSPEED) * fs;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
/// <summary>
|
||||
/// TBP GPU代码
|
||||
/// </summary>
|
||||
/// <param name="antpos_ptr">卫星轨道坐标</param>
|
||||
/// <param name="echoArr">回波矩阵</param>
|
||||
/// <param name="img_arr">图像矩阵</param>
|
||||
void TBPImageGPUAlgBlock(std::shared_ptr<float> antPx, std::shared_ptr<float> antPy, std::shared_ptr<float> antPz, // 天线坐标
|
||||
std::shared_ptr<float> antVx, std::shared_ptr<float> antVy, std::shared_ptr<float> antVz,
|
||||
std::shared_ptr<float> img_x, std::shared_ptr<float> img_y, std::shared_ptr<float> img_z, // 图像坐标
|
||||
std::shared_ptr<std::complex<float>> echoArr,std::shared_ptr<std::complex<float>> img_arr,
|
||||
float freq, float fs,float Rnear,float Rfar,
|
||||
long rowcount,long colcount) {
|
||||
|
||||
float factorj = freq * 4 * PI / LIGHTSPEED;
|
||||
std::shared_ptr<float> imgR(new float[rowcount * colcount]);
|
||||
std::shared_ptr<long> imgEchoID(new long[rowcount * colcount]);
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
for (long prfid = 0; prfid < rowcount; prfid++) {
|
||||
for (long i = 0; i < rowcount; i++) {
|
||||
for (long j = 0; j < colcount; j++) {
|
||||
imgR.get()[i * colcount + j] = std::sqrt(
|
||||
std::pow(antPx.get()[prfid]-img_x.get()[i * colcount + j], 2)
|
||||
+ std::pow(antPy.get()[prfid] - img_y.get()[i * colcount + j], 2)
|
||||
+ std::pow(antPz.get()[prfid] - img_z.get()[i * colcount + j], 2)
|
||||
);
|
||||
imgEchoID.get()[i * colcount + j] = (imgR.get()[i * colcount + j] - Rnear) / 2 / LIGHTSPEED * fs;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// 分配主机内存
|
||||
float* h_antPx, * h_antPy, * h_antPz, * h_img_x, * h_img_y, * h_img_z;
|
||||
float* h_imgR, * h_imgEchoID;
|
||||
cudaMallocHost(&h_antPx, sizeof(float) * 1); // 单个传感器的位置
|
||||
cudaMallocHost(&h_antPy, sizeof(float) * 1);
|
||||
cudaMallocHost(&h_antPz, sizeof(float) * 1);
|
||||
cudaMallocHost(&h_img_x, sizeof(float) * rowcount * colcount);
|
||||
cudaMallocHost(&h_img_y, sizeof(float) * rowcount * colcount);
|
||||
cudaMallocHost(&h_img_z, sizeof(float) * rowcount * colcount);
|
||||
cudaMallocHost(&h_imgR, sizeof(float) * rowcount * colcount);
|
||||
cudaMallocHost(&h_imgEchoID, sizeof(float) * rowcount * colcount);
|
||||
|
||||
// 将数据初始化到主机内存（这里仅为示例，实际数据根据需求加载）
|
||||
for (long i = 0; i < rowcount * colcount; i++) {
|
||||
h_img_x[i] = 0.0f;
|
||||
h_img_y[i] = 0.0f;
|
||||
h_img_z[i] = 0.0f;
|
||||
}
|
||||
h_antPx[0] = 0.0f;
|
||||
h_antPy[0] = 0.0f;
|
||||
h_antPz[0] = 0.0f;
|
||||
|
||||
// 分配设备内存
|
||||
float* d_antPx, * d_antPy, * d_antPz, * d_img_x, * d_img_y, * d_img_z;
|
||||
float* d_imgR, * d_imgEchoID;
|
||||
|
||||
cudaMalloc(&d_antPx, sizeof(float) * 1);
|
||||
cudaMalloc(&d_antPy, sizeof(float) * 1);
|
||||
cudaMalloc(&d_antPz, sizeof(float) * 1);
|
||||
cudaMalloc(&d_img_x, sizeof(float) * rowcount * colcount);
|
||||
cudaMalloc(&d_img_y, sizeof(float) * rowcount * colcount);
|
||||
cudaMalloc(&d_img_z, sizeof(float) * rowcount * colcount);
|
||||
cudaMalloc(&d_imgR, sizeof(float) * rowcount * colcount);
|
||||
cudaMalloc(&d_imgEchoID, sizeof(float) * rowcount * colcount);
|
||||
|
||||
// 将数据从主机拷贝到设备
|
||||
cudaMemcpy(d_antPx, h_antPx, sizeof(float) * 1, cudaMemcpyHostToDevice);
|
||||
cudaMemcpy(d_antPy, h_antPy, sizeof(float) * 1, cudaMemcpyHostToDevice);
|
||||
cudaMemcpy(d_antPz, h_antPz, sizeof(float) * 1, cudaMemcpyHostToDevice);
|
||||
cudaMemcpy(d_img_x, h_img_x, sizeof(float) * rowcount * colcount, cudaMemcpyHostToDevice);
|
||||
cudaMemcpy(d_img_y, h_img_y, sizeof(float) * rowcount * colcount, cudaMemcpyHostToDevice);
|
||||
cudaMemcpy(d_img_z, h_img_z, sizeof(float) * rowcount * colcount, cudaMemcpyHostToDevice);
|
||||
|
||||
// 设置 CUDA 核函数的网格和块的尺寸
|
||||
dim3 blockDim(16, 16); // 每个块处理 16x16 的像素点
|
||||
dim3 gridDim((colcount + blockDim.x - 1) / blockDim.x,
|
||||
(rowcount + blockDim.y - 1) / blockDim.y);
|
||||
|
||||
// 调用 CUDA 核函数
|
||||
computeDistanceAndEchoID << <gridDim, blockDim >> > (d_antPx, d_antPy, d_antPz,
|
||||
d_img_x, d_img_y, d_img_z,
|
||||
d_imgR, d_imgEchoID,
|
||||
rowcount, colcount, prfid,
|
||||
RNEAR, LIGHTSPEED, FS);
|
||||
|
||||
// 等待所有设备任务完成
|
||||
cudaDeviceSynchronize();
|
||||
|
||||
// 将结果从设备拷贝回主机
|
||||
cudaMemcpy(h_imgR, d_imgR, sizeof(float) * rowcount * colcount, cudaMemcpyDeviceToHost);
|
||||
cudaMemcpy(h_imgEchoID, d_imgEchoID, sizeof(float) * rowcount * colcount, cudaMemcpyDeviceToHost);
|
||||
|
||||
// 清理资源
|
||||
cudaFree(d_antPx);
|
||||
cudaFree(d_antPy);
|
||||
cudaFree(d_antPz);
|
||||
cudaFree(d_img_x);
|
||||
cudaFree(d_img_y);
|
||||
cudaFree(d_img_z);
|
||||
cudaFree(d_imgR);
|
||||
cudaFree(d_imgEchoID);
|
||||
cudaFreeHost(h_antPx);
|
||||
cudaFreeHost(h_antPy);
|
||||
cudaFreeHost(h_antPz);
|
||||
cudaFreeHost(h_img_x);
|
||||
cudaFreeHost(h_img_y);
|
||||
cudaFreeHost(h_img_z);
|
||||
cudaFreeHost(h_imgR);
|
||||
cudaFreeHost(h_imgEchoID);
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -6,6 +6,8 @@
|
|||
#include <cmath>
|
||||
#include <QProgressDialog>
|
||||
|
||||
|
||||
|
||||
void TBPImageProcess(QString echofile, QString outImageFolder, QString imagePlanePath,long num_thread)
|
||||
{
|
||||
std::shared_ptr<EchoL0Dataset> echoL0ds(new EchoL0Dataset);
|
||||
|
|
@ -63,7 +65,7 @@ std::shared_ptr<SARSimulationImageL1Dataset> TBPImageAlgCls::getImageL0()
|
|||
ErrorCode TBPImageAlgCls::Process(long num_thread)
|
||||
{
|
||||
if (GPURUN) {
|
||||
|
||||
return this->ProcessGPU();
|
||||
}
|
||||
else {
|
||||
return this->ProcessCPU(num_thread);
|
||||
|
|
@ -337,36 +339,88 @@ ErrorCode TBPImageAlgCls::ProcessGPU()
|
|||
pixelZ.get()[i*colCount+ j] = Pz + AntDirectZ * R;
|
||||
}
|
||||
}
|
||||
|
||||
qDebug()<<"R: " << R;
|
||||
this->L1ds->saveAntPos(antpos);
|
||||
antpos.reset();
|
||||
}
|
||||
|
||||
|
||||
std::shared_ptr<std::complex<float>> imagarr(new std::complex<float>[rowCount * colCount]);
|
||||
{
|
||||
|
||||
std::shared_ptr<std::complex<double>> Rasterarr = this->L1ds->getImageRaster();
|
||||
for (long i = 0; i < pixelCount; i++) {
|
||||
imagarr.get()[i] = Rasterarr.get()[i];
|
||||
}
|
||||
}
|
||||
|
||||
std::shared_ptr<std::complex<float>> echodata(new std::complex<float>[rowCount * colCount]);
|
||||
{
|
||||
std::shared_ptr<std::complex<double>> echodataPtr = this->L0ds->getEchoArr();
|
||||
for (long i = 0; i < PRFCount; i++) {
|
||||
for (long j = 0; j < PlusePoints; j++) {
|
||||
echodata.get()[i * PlusePoints + j] = echodataPtr.get()[i * PlusePoints + j];
|
||||
|
||||
for (long i = 0; i < rowCount; i++) {
|
||||
for (long j = 0; j < colCount; j++) {
|
||||
Rasterarr.get()[i * colCount + j] = Rasterarr.get()[i * colCount + j] * 0.0;
|
||||
}
|
||||
}
|
||||
}
|
||||
TBPImageGPUAlg(Pxs, Pys, Pzs, // ÌìÏß×ø±ê
|
||||
Vxs, Vys, Vzs,
|
||||
pixelX, pixelY, pixelZ, // ͼÏñ×ø±ê
|
||||
echodata, imagarr, freq,fs,Rnear,Rfar,rowCount,colCount);
|
||||
|
||||
|
||||
TBPImageGPUAlg(Pxs, Pys, Pzs, // 天线坐标
|
||||
Vxs, Vys, Vzs,
|
||||
pixelX, pixelY, pixelZ, // 图像坐标
|
||||
echodataPtr, Rasterarr,
|
||||
freq, fs, Rnear, Rfar,
|
||||
rowCount, colCount, this->L1ds);
|
||||
|
||||
qDebug() << "image writing:\t" << this->L1ds->getxmlFilePath();
|
||||
|
||||
return ErrorCode::SUCCESS;
|
||||
}
|
||||
|
||||
void TBPImageAlgCls::setGPU(bool flag)
|
||||
{
|
||||
this->GPURUN = flag;
|
||||
}
|
||||
|
||||
bool TBPImageAlgCls::getGPU( )
|
||||
{
|
||||
return this->GPURUN;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// TBP GPU代码
|
||||
/// </summary>
|
||||
/// <param name="antpos_ptr">卫星轨道坐标</param>
|
||||
/// <param name="echoArr">回波矩阵</param>
|
||||
/// <param name="img_arr">图像矩阵</param>
|
||||
void TBPImageGPUAlg(std::shared_ptr<float> antPx, std::shared_ptr<float> antPy, std::shared_ptr<float> antPz, // 天线坐标
|
||||
std::shared_ptr<float> antVx, std::shared_ptr<float> antVy, std::shared_ptr<float> antVz,
|
||||
std::shared_ptr<float> img_x, std::shared_ptr<float> img_y, std::shared_ptr<float> img_z, // 图像坐标
|
||||
std::shared_ptr<std::complex<double>> echoArr, std::shared_ptr<std::complex<double>> img_arr,
|
||||
float freq, float fs, float Rnear, float Rfar,
|
||||
long rowcount, long colcount, std::shared_ptr<SARSimulationImageL1Dataset> L1ds) {
|
||||
|
||||
float factorj = freq * 4 * PI / LIGHTSPEED;
|
||||
|
||||
|
||||
qDebug() << "factorj:\t" << factorj;
|
||||
qDebug() << "freq:\t" << freq;
|
||||
qDebug() << "fs:\t" << fs;
|
||||
qDebug() << "Rnear:\t" << Rnear;
|
||||
qDebug() << "Rfar:\t" << Rfar;
|
||||
|
||||
qDebug() << "img_x:\t" << img_x.get()[0];
|
||||
qDebug() << "img_y:\t" << img_y.get()[0];
|
||||
qDebug() << "img_z:\t" << img_z.get()[0];
|
||||
|
||||
|
||||
long blockline = Memory1MB * 1000 / sizeof(float) / colcount;
|
||||
blockline = blockline < 10 ? 10 : blockline;
|
||||
for (long startline = 0; startline < rowcount; startline = startline + blockline) {
|
||||
long stepline = startline + blockline < rowcount ? blockline : rowcount - startline;
|
||||
std::cout << startline << " \ " << rowcount << " "<< stepline << " start " << std::endl;
|
||||
//TBPImageGPUBlock(antPx.get(), antPy.get(), antPz.get(), img_x.get(), img_y.get(), img_z.get(),
|
||||
// echoArr, rowcount, colcount,
|
||||
// img_arr,
|
||||
// freq, fs, Rnear, Rfar, factorj, startline, stepline,
|
||||
// stepline, colcount);
|
||||
//std::cout << startline << " \ " << rowcount << " " << stepline << " end " << std::endl;
|
||||
//L1ds->saveImageRaster(img_arr, 0, rowcount);
|
||||
}
|
||||
L1ds->saveImageRaster(img_arr, 0, rowcount);
|
||||
|
||||
L1ds->saveToXml();
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -50,12 +50,21 @@ public:
|
|||
|
||||
public:
|
||||
ErrorCode Process(long num_thread);
|
||||
|
||||
void setGPU(bool flag);
|
||||
bool getGPU( );
|
||||
private:
|
||||
ErrorCode ProcessCPU(long num_thread);
|
||||
|
||||
ErrorCode ProcessGPU();
|
||||
|
||||
|
||||
};
|
||||
|
||||
|
||||
void TBPImageProcess(QString echofile,QString outImageFolder,QString imagePlanePath,long num_thread);
|
||||
void TBPImageProcess(QString echofile,QString outImageFolder,QString imagePlanePath,long num_thread);
|
||||
void TBPImageGPUAlg(std::shared_ptr<float> antPx, std::shared_ptr<float> antPy, std::shared_ptr<float> antPz, // ÌìÏß×ø±ê
|
||||
std::shared_ptr<float> antVx, std::shared_ptr<float> antVy, std::shared_ptr<float> antVz,
|
||||
std::shared_ptr<float> img_x, std::shared_ptr<float> img_y, std::shared_ptr<float> img_z, // ͼÏñ×ø±ê
|
||||
std::shared_ptr<std::complex<double>> echoArr, std::shared_ptr<std::complex<double>> img_arr,
|
||||
float freq, float fs, float Rnear, float Rfar,
|
||||
long rowcount, long colcount, std::shared_ptr<SARSimulationImageL1Dataset> L1ds);
|
||||
Binary file not shown.
|
|
@ -0,0 +1,21 @@
|
|||
%% WGS84直角坐标系转经纬度
|
||||
% 东经正数，西经为负数
|
||||
% 北纬为正数，南纬为负数
|
||||
% 输入参数1：纬度；输入参数2：经度；输入参数3：高度
|
||||
% 经纬度单位：度；高度单位：米
|
||||
function [latitude,longitude,height]=XYZ2LLA(x,y,z)
|
||||
|
||||
a = 6378137.0;
|
||||
b = 6356752.31424518;
|
||||
c = sqrt(((a * a) - (b * b)) / (a * a));
|
||||
d = sqrt(((a * a) - (b * b)) / (b * b));
|
||||
p = sqrt((x * x) + (y * y));
|
||||
q = atan((z * a)/ (p * b));
|
||||
longitude = atan(y/x);
|
||||
latitude = atan((z + (d * d) * b * sin(q)^3)/(p - (c * c) * a * cos(q)^3));
|
||||
N = a / sqrt(1 - ((c * c) * sin(latitude)^2));
|
||||
height = (p / cos(latitude)) - N;
|
||||
longitude = longitude * 180.0 / pi+180;
|
||||
latitude = latitude * 180.0 / pi;
|
||||
end
|
||||
|
||||
|
|
@ -0,0 +1,226 @@
|
|||
% n0= [-1944902.1250000000 4077521.2500000000 4487326.5000000000 ];
|
||||
% n1= [-1944873.0000000000 4077511.0000000000 4487348.5000000000 ];
|
||||
% n2= [-1944922.0000000000 4077512.0000000000 4487326.5000000000 ];
|
||||
% n3= [-1944931.3750000000 4077531.5000000000 4487305.0000000000 ];
|
||||
% n4= [-1944882.3750000000 4077530.7500000000 4487326.5000000000 ];
|
||||
%
|
||||
% n01 = n1 - n0, n02 = n2 - n0, n03 = n3 - n0, n04 = n4 - n0;
|
||||
%
|
||||
% VN=(cross(n01, n04) + cross(n04, n03) + cross(n03, n02) + cross(n02, n01)) * 0.25;
|
||||
% VNorm=VN./norm(VN);
|
||||
clear,clc,close all
|
||||
|
||||
|
||||
% 打开二进制文件
|
||||
fileID = fopen('D:\\Programme\\vs2022\\RasterMergeTest\\TestData\\outData\\GF3_Simulation.gpspos.data', 'rb'); % 假设二进制文件名为 'data.bin'
|
||||
|
||||
% 定义每个数据字段的数据类型
|
||||
% 假设每个数据是双精度浮动数（8字节）
|
||||
dataType = 'double';
|
||||
|
||||
% 读取数据：根据你的数据格式，数据长度为 16 * 8 字节（每个元素8字节）
|
||||
% 例如：每16个数据项为一组（对应prf_id）
|
||||
numFields = 19; % 每组数据的字段数
|
||||
PRFnumRecords = 15382; % 假设有100个记录，你可以根据实际情况修改
|
||||
|
||||
% 读取所有记录的数据
|
||||
rawData = fread(fileID, numFields * PRFnumRecords, dataType);
|
||||
|
||||
% 关闭文件
|
||||
fclose(fileID);
|
||||
|
||||
% 解析数据为结构体数组
|
||||
data = struct();
|
||||
AntDirecX=zeros(PRFnumRecords,1);
|
||||
AntDirecY=zeros(PRFnumRecords,1);
|
||||
AntDirecZ=zeros(PRFnumRecords,1);
|
||||
Px=zeros(PRFnumRecords,1);
|
||||
Py=zeros(PRFnumRecords,1);
|
||||
Pz=zeros(PRFnumRecords,1);
|
||||
|
||||
Vx=zeros(PRFnumRecords,1);
|
||||
Vy=zeros(PRFnumRecords,1);
|
||||
Vz=zeros(PRFnumRecords,1);
|
||||
|
||||
% 假设你的数据在 rawData 中按顺序排列
|
||||
% 例如，解析 prf_id = 0, 1, 2...的相关数据
|
||||
for prf_id = 0:PRFnumRecords-1
|
||||
idx = prf_id * numFields + 1;
|
||||
data(prf_id+1).prf_time = rawData(idx); % prf_time + imageStarttime
|
||||
Px(prf_id+1) = rawData(idx+1); % sateOirbtNode.Px
|
||||
Py(prf_id+1) = rawData(idx+2); % sateOirbtNode.Py
|
||||
Pz(prf_id+1) = rawData(idx+3); % sateOirbtNode.Pz
|
||||
Vx(prf_id+1) = rawData(idx+4); % sateOirbtNode.Vx
|
||||
Vy(prf_id+1) = rawData(idx+5); % sateOirbtNode.Vy
|
||||
Vz(prf_id+1) = rawData(idx+6); % sateOirbtNode.Vz
|
||||
AntDirecX(prf_id+1) = rawData(idx+7); % sateOirbtNode.AntDirecX
|
||||
AntDirecY(prf_id+1) = rawData(idx+8); % sateOirbtNode.AntDirecY
|
||||
AntDirecZ(prf_id+1) = rawData(idx+9); % sateOirbtNode.AntDirecZ
|
||||
data(prf_id+1).AVx = rawData(idx+10); % sateOirbtNode.AVx
|
||||
data(prf_id+1).AVy = rawData(idx+11); % sateOirbtNode.AVy
|
||||
data(prf_id+1).AVz = rawData(idx+12); % sateOirbtNode.AVz
|
||||
data(prf_id+1).lon = rawData(idx+13); % outP.lon
|
||||
data(prf_id+1).lat = rawData(idx+14); % outP.lat
|
||||
data(prf_id+1).ati = rawData(idx+15); % outP.ati
|
||||
end
|
||||
|
||||
% 查看解析后的数据
|
||||
disp(data);
|
||||
|
||||
|
||||
|
||||
fileID = fopen('D:\\Programme\\vs2022\\RasterMergeTest\\TestData\\outData\\tmp\\ImageX.dat', 'rb'); % 假设二进制文件名为 'data.bin'
|
||||
dataType = 'float';
|
||||
numFields = 5400; % 每组数据的字段数
|
||||
numRecords = 5400; % 假设有100个记录，你可以根据实际情况修改
|
||||
demX = fread(fileID, numFields * numRecords, dataType);
|
||||
demX=reshape(demX,[numRecords,numRecords]);
|
||||
fclose(fileID);
|
||||
|
||||
|
||||
fileID = fopen('D:\\Programme\\vs2022\\RasterMergeTest\\TestData\\outData\\tmp\\ImageY.dat', 'rb'); % 假设二进制文件名为 'data.bin'
|
||||
dataType = 'float';
|
||||
numFields = 5400; % 每组数据的字段数
|
||||
numRecords = 5400; % 假设有100个记录，你可以根据实际情况修改
|
||||
demY = fread(fileID, numFields * numRecords, dataType);
|
||||
demY=reshape(demY,[numRecords,numRecords]);
|
||||
fclose(fileID);
|
||||
|
||||
fileID = fopen('D:\\Programme\\vs2022\\RasterMergeTest\\TestData\\outData\\tmp\\ImageZ.dat', 'rb'); % 假设二进制文件名为 'data.bin'
|
||||
dataType = 'float';
|
||||
numFields = 5400; % 每组数据的字段数
|
||||
numRecords = 5400; % 假设有100个记录，你可以根据实际情况修改
|
||||
demZ = fread(fileID, numFields * numRecords, dataType);
|
||||
demZ=reshape(demZ,[numRecords,numRecords]);
|
||||
fclose(fileID);
|
||||
|
||||
|
||||
%% 绘制成像平面
|
||||
Rn=884053;
|
||||
Rf=942069;
|
||||
|
||||
|
||||
data_ant_norm=(AntDirecX.^2+AntDirecY.^2+AntDirecZ.^2).^0.5;
|
||||
|
||||
AntDirecX=AntDirecX./data_ant_norm;
|
||||
AntDirecY=AntDirecY./data_ant_norm;
|
||||
AntDirecZ=AntDirecZ./data_ant_norm;
|
||||
|
||||
Rlist=linspace(Rn,Rf,300);
|
||||
ImageX=zeros(PRFnumRecords,300);
|
||||
ImageY=zeros(PRFnumRecords,300);
|
||||
ImageZ=zeros(PRFnumRecords,300);
|
||||
|
||||
for i=1:PRFnumRecords
|
||||
ImageX(i,:)=Px(i,:) + Rlist.*AntDirecX(i,:);
|
||||
ImageY(i,:)=Py(i,:) + Rlist.*AntDirecY(i,:);
|
||||
ImageZ(i,:)=Pz(i,:) + Rlist.*AntDirecZ(i,:);
|
||||
end
|
||||
B=[AntDirecX(1,1),AntDirecY(1,1),AntDirecZ(1,1)];
|
||||
A=[Px(1,1)-Px(2,1),Py(1,1)-Py(2,1),Pz(1,1)-Pz(2,1)] ;
|
||||
V=[Vx(1,1),Vy(1,1),Vz(1,1)];
|
||||
% 计算点积
|
||||
dotProduct = dot(A,B);
|
||||
|
||||
% 计算模长
|
||||
normA = norm(A);
|
||||
normB = norm(B);
|
||||
|
||||
% 计算夹角的余弦值
|
||||
cosTheta = acos(dotProduct / (normA * normB));
|
||||
cosTheta*180/pi
|
||||
|
||||
tp=[-2023401.250000,4104205.500000,4428297.000000];
|
||||
TP=Px.*0;
|
||||
for i=1:PRFnumRecords
|
||||
TP(i,:)=sqrt((Px(i,:) -tp(1)).^2+(Py(i,:) -tp(2)).^2+(Pz(i,:) -tp(3)).^2);
|
||||
|
||||
end
|
||||
|
||||
figure,
|
||||
plot(TP )
|
||||
|
||||
|
||||
|
||||
PR=(Px.^2+Py.^2+Pz.^2).^0.5;
|
||||
|
||||
|
||||
|
||||
ImageR=(ImageX.^2+ImageY.^2+ImageZ.^2).^0.5;
|
||||
|
||||
|
||||
figure,
|
||||
plot(ImageR(:,1)),hold on
|
||||
plot(ImageR(:,end)),hold on
|
||||
plot(PR),hold on
|
||||
legend(["Rn","Rf","Rs"])
|
||||
|
||||
a=[Px-mean(demX,"all"),Py-mean(demY,"all"),Pz-mean(demZ,"all")];
|
||||
a=(a(:,1).^2+a(:,2).^2+a(:,3).^2).^0.5;
|
||||
|
||||
|
||||
|
||||
figure,
|
||||
surfl(ImageX,ImageY,ImageZ),shading flat,hold on
|
||||
scatter3(mean(demX,"all"),mean(demY,"all"),mean(demZ,"all"),'red','filled'),shading flat,hold on
|
||||
plot3(Px,Py,Pz,'red')
|
||||
xlabel("X")
|
||||
ylabel("Y")
|
||||
zlabel("Z")
|
||||
view([-90.6000010944003 90]);
|
||||
|
||||
|
||||
dl=sqrt((Px(1)-Px(end)).^2+(Py(1)-Py(end)).^2+(Pz(1)-Pz(end)).^2);
|
||||
|
||||
R=7;
|
||||
ImageR=((Px(10000,1)-R-Px).^2+(Py(10000,1)-R-Py).^2+(Pz(10000,1)-R-Pz).^2).^0.5;
|
||||
figure,
|
||||
plot(ImageR)
|
||||
|
||||
|
||||
P=zeros(100*100,3);
|
||||
pp=1;
|
||||
for i=1:100
|
||||
for j=1:100
|
||||
[latitude,longitude,height]=XYZ2LLA(ImageX(i,j),ImageY(i,j),ImageZ(i,j));
|
||||
P(pp,:)=[latitude,longitude,height];
|
||||
pp=pp+1;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
% %% 计算
|
||||
%
|
||||
%
|
||||
% Tx=-2001212.875000;
|
||||
% Ty=4082207.000000;
|
||||
% Tz=4471714.500000;
|
||||
% R=((demX-Px(1)).^2+(demY-Py(1)).^2+(demZ-Pz(1)).^2).^0.5;
|
||||
% figure,
|
||||
% plot(R)
|
||||
%
|
||||
% imgX=ImageX(3000,150);
|
||||
% imgY=ImageY(3000,150);
|
||||
% imgZ=ImageZ(3000,150);
|
||||
% R1= sqrt((ImageX(1,1)-Px(:,1)).^2+(ImageY(1,1)-Py(:,1)).^2+(ImageZ(1,1)-Pz(:,1)).^2);
|
||||
% figure,
|
||||
% plot(R1)
|
||||
%
|
||||
%
|
||||
%
|
||||
% Timgx=abs(ImageX-Tx);
|
||||
% Timgy=abs(ImageY-Ty);
|
||||
% Timgz=abs(ImageZ-Tz);
|
||||
%
|
||||
% [latitude,longitude,height]=XYZ2LLA(ImageX(3000,150),ImageY(3000,150),ImageZ(3000,150))
|
||||
%
|
||||
%
|
||||
%
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,141 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "7d84d0c7-8625-432c-acd7-89bdb0e66a43",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from osgeo import gdal\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"def crop_raster(input_raster_path, output_raster_path, bounds):\n",
|
||||
" \"\"\"\n",
|
||||
" 使用 GDAL 裁剪栅格影像到指定的四至范围。\n",
|
||||
"\n",
|
||||
" :param input_raster_path: 输入栅格影像路径\n",
|
||||
" :param output_raster_path: 输出裁剪后影像的路径\n",
|
||||
" :param bounds: 裁剪范围，格式为 (minx, miny, maxx, maxy)\n",
|
||||
" \"\"\"\n",
|
||||
" minx, miny, maxx, maxy = bounds\n",
|
||||
"\n",
|
||||
" # 打开输入影像\n",
|
||||
" dataset = gdal.Open(input_raster_path)\n",
|
||||
" if dataset is None:\n",
|
||||
" print(f\"无法打开输入影像: {input_raster_path}\")\n",
|
||||
" return\n",
|
||||
"\n",
|
||||
" # 获取影像的投影和变换信息\n",
|
||||
" geo_transform = dataset.GetGeoTransform()\n",
|
||||
" projection = dataset.GetProjection()\n",
|
||||
"\n",
|
||||
" # 计算裁剪区域在影像中的像素坐标\n",
|
||||
" ulx = int((minx - geo_transform[0]) / geo_transform[1]) # 左上角 x\n",
|
||||
" uly = int((maxy - geo_transform[3]) / geo_transform[5]) # 左上角 y\n",
|
||||
" lrx = int((maxx - geo_transform[0]) / geo_transform[1]) # 右下角 x\n",
|
||||
" lry = int((miny - geo_transform[3]) / geo_transform[5]) # 右下角 y\n",
|
||||
"\n",
|
||||
" # 检查像素坐标是否在影像范围内\n",
|
||||
" if ulx < 0 or uly < 0 or lrx > dataset.RasterXSize or lry > dataset.RasterYSize:\n",
|
||||
" print(\"裁剪范围超出影像范围！\")\n",
|
||||
" return\n",
|
||||
"\n",
|
||||
" # 创建输出影像\n",
|
||||
" driver = gdal.GetDriverByName('GTiff')\n",
|
||||
" out_dataset = driver.Create(output_raster_path, lrx - ulx, lry - uly, dataset.RasterCount, gdal.GDT_Float32)\n",
|
||||
"\n",
|
||||
" # 设置输出影像的投影和变换信息\n",
|
||||
" out_geo_transform = (minx, geo_transform[1], geo_transform[2], maxy, geo_transform[4], geo_transform[5])\n",
|
||||
" out_dataset.SetGeoTransform(out_geo_transform)\n",
|
||||
" out_dataset.SetProjection(projection)\n",
|
||||
"\n",
|
||||
" # 将裁剪后的数据写入输出影像\n",
|
||||
" for i in range(1, dataset.RasterCount + 1):\n",
|
||||
" band = dataset.GetRasterBand(i)\n",
|
||||
" data = band.ReadAsArray(ulx, uly, lrx - ulx, lry - uly)\n",
|
||||
" out_dataset.GetRasterBand(i).WriteArray(data)\n",
|
||||
"\n",
|
||||
" # 清理\n",
|
||||
" out_dataset.FlushCache()\n",
|
||||
" del out_dataset\n",
|
||||
" del dataset\n",
|
||||
"\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "3ac1f4a3-38f8-4c50-8bad-36852fcb18f6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 输入栅格影像路径\n",
|
||||
"input_raster = \"115E39N_COP30.tif\"\n",
|
||||
"\n",
|
||||
"# 输出裁剪后影像的路径\n",
|
||||
"output_raster = \"115E39N_COP30_clip.tif\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# 定义裁剪范围 (minx, miny, maxx, maxy)\n",
|
||||
"bounds =(115.5,43.5,117.0,45.0) # 示例范围\n",
|
||||
"\n",
|
||||
"# 裁剪影像\n",
|
||||
"crop_raster(input_raster, output_raster, bounds)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "65dcf0f1-55e7-4dfa-8988-412ca0e6c41a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"(xmin, ymin, xmax, ymax)=(115.5,43.5,117.0,45.0)\n",
|
||||
"# 定义裁剪范围 (minx, miny, maxx, maxy)\n",
|
||||
"bounds = (xmin, ymin, xmax, ymax) # 例如 (100000, 200000, 110000, 210000)\n",
|
||||
"# 裁剪影像\n",
|
||||
"clip_raster(input_raster, output_raster, bounds)\n",
|
||||
"print(f\"影像已裁剪并保存到: {output_raster}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ea8bcfa3-7612-477d-bf0b-b4e3537fcf33",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "407416e7-729d-4d7e-ae09-318cdfce7739",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.19"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue